Vaginal discharge, burning, itching, and malodor are the most common vaginal symptoms reported by reproductive-age women resulting in millions of health care visits annually in the United States alone. These symptoms create substantial discomfort, are often recurrent, and negatively impact women's self- esteem and quality of life. Often, women resort to ill-advised alternative treatments in an attempt to ameliorate their symptoms that only provide a short-lived relief, and in many cases increase symptom severity. These symptoms are associated with numerous serious gynecological and obstetric outcomes, including an increased risk for sexually transmitted infections. The long-term goal of the proposed work is to develop a more complete understanding of the underlying causes of vaginal symptoms so that targeted and effective strategies can be developed to effectively prevent or treat vaginal discharge, discomfort, and malodor. The multi-omics datasets we propose to gather and analyze will allow us to test the central hypothesis that symptoms are emergent properties of the vaginal microbiome that result from the interplay of specific functions, and not simply microbial composition of vaginal microbiomes and host responses elicit these signs and symptoms. To achieve this, we will leverage a unique set of vaginal swabs samples and extensive metadata that were prospectively collected daily by 135 women for 10 weeks. During this study, women experienced either 1) clear episodes of vaginal symptoms, 2) chronic and persistent symptoms or 3) no symptoms. The study design affords a unique opportunity to use `omics technologies on samples collected before, during and after episode of vaginal symptoms and compare these to women with chronic or no symptoms, and identify specific predictive biomarkers that will translate to more personalized management of women's health. The proposed project will address two specific aims: (1) Determine the composition and function of the vaginal microbiome by characterizing bacterial metagenomes, the host and bacterial transcriptomes and metabolomes, and markers of innate immunity in vaginal samples collected prior to, during and after episodes of vaginal symptoms and compare these to comparable data from asymptomatic women. (2) Develop predictive and causal models of symptom onset using integrative systems biology approaches using a multi- prong modeling strategy that includes lasso regression, ridge regression and elactic net regression combined with robust and modern model selection techniques, and network analyses, we will achieve a predictive and causal model that is a balance of robustness, explanatory power, and size (in terms of the number of predictor variables). Armed with detailed knowledge of changes in genomic factor of the microbiota and the host during the onset and recovery from vaginal symptoms it will be possible to develop more accurate and sensitive diagnostic procedures, new therapeutic strategies, and effective means to insure vaginal homeostasis.